The present invention relates to valve diagnostics, and more particularly, to the diagnosis of valves that are installed in process plant flow lines.
Many types of solenoid and control valves are typically present in process plants dedicated, for example, to producing electrical power, refining materials, or producing food. In many such plants, reliable valve operation not only affects the efficiency of the process or the quality of the product, but may also have severe safety consequences. Safety considerations are particularly relevant in nuclear power plants.
Accordingly, it is desirable that some indicator of reliability be obtainable from measurable characteristics of the valve while installed in the flow line, i.e., without removing, disassembling, inspecting, reassembling, and reinstalling the valve. In this context, reliability refers not only to the availability of the valve to operate when actuated, but also the effectiveness of the operation, i.e., stroking from a fully open to a fully closed position when energized within specified limits.
A known approach to such diagnostics includes energizing the valve while obtaining accurate measurements of, for example, stem thrust or displacement. By analyzing the relationship of stem thrust, movement, or similar dependent variable, to the independent energizing variable, such as electric current, hydraulic pressure, or pneumatic pressure in the actuator, certain valve behaviors indicative of reliability can be inferred. Conventionally, such diagnostic techniques rely on the connection of specially adapted sensors to the individual valve or its associated components, with the sensor output delivered to a portable data acquisition unit which is temporarily located in the vicinity of the valve.
With a growing desire to reduce the time required to obtain data from many valves in the plant, and to obtain more kinds of data that are useful for diagnostic purposes, the need has arisen for greater flexibility of the equipment and methods utilized to acquire diagnostic data.